Research Area: Access Networks

We deal within the CAN group with the research field of "Access Networks".

Access networks represent a very focused area in the research field of communication networks. In access networks, there are important communication paradigms that are subject of research in the CAN group. We examine the performance of communication processes and principles with means of modeling and performance analysis.

Study on Wifi Offloading

WiFi offloading is a current trend to cope with the demands of mobile users and the load on cellular networks. It allows providers to handle the traffic in well-dimensioned fixed networks and thereby save costs. In addition, end users can benefit from higher throughput and avoid exceeding their data plan.

We develop simulative and analytic models to investigate the potential of WiFi offloading to mitigate load of cellular networks. We evaluate its impact on QoE and energy efficiency for video streaming applications.

Offloading mobile Internet data via WiFi has emerged as an omnipresent trend. WiFi networks are already widely deployed by many private and public institutions (e.g., libraries, cafes, restaurants) but also by commercial services to provide alternative Internet access for their customers and to mitigate the load on mobile networks. Moreover, smart cities start to install WiFi infrastructure for current and future civic services, e.g., based on sensor networks or the Internet of Things. A simple model for the distribution of WiFi hotspots in an urban environment is presented. The hotspot locations are modeled with a uniform distribution of the angle and an exponential distribution of the distance, which is truncated to the city limits. We compare the characteristics of this model in detail to the real distributions. Moreover, we show the applicability and the limitations of this model, and the results suggest that the model can be used in scenarios, which do not require an accurate spatial collocation of the hotspots, such as offloading potential, coverage, or signal strength.

In a recent trend to lessen the load on cellular networks in cities, users are offered to offload mobile connections to lower cost WiFi networks. In this work, we conduct a simulative performance evaluation of the impact of WiFi offloading for a mobile end user of a HTTP adaptive video streaming (HAS) service depending on availability and range of the WiFi hotspots. The simulation is based on connectivity measurements from a German city and evaluates the key performance indicators for the QoE of HAS, i.e., initial delay, stalling, and quality adaptation. Additionally, a smartphone energy model is applied to assess the energy consumption during the streaming. The results indicate that WiFi offloading of HAS connections to public WiFi hotspots is not attractive for end users both in terms of QoE and energy consumption. However, it can be shown that WiFi offloading can be beneficial also for end users in case high bandwidths can be received via WiFi.

WiFi offloading has become increasingly popular. Many private and public institutions (e.g., libraries, cafes, restaurants) already provide an alternative free Internet link via WiFi, but also commercial services emerge to mitigate the load on mobile networks. Moreover, smart cities start to establish WiFi infrastructure for current and future civic services. In this work, the hotspot locations of ten diverse large cities are characterized, and a surprisingly simple model for the distribution of WiFi hotspots in an urban environment is derived.

The load on cellular networks is constantly increasing. Especially video streaming applications, whose demands and requirements keep growing, put high loads on cellular networks. A solution to mitigate the cellular load in urban environments is offloading mobile connections to WiFi access points, which is followed by many providers recently. Because of the large number of mobile users and devices there is also a high potential to save energy by WiFi offloading. In this work, we develop a model to assess the energy consumption of mobile devices during video sessions. We evaluate the potential of WiFi offloading in an urban environment and the implications of offloading connections on energy consumption of mobile devices. Our results show that, although WiFi is more energy efficient than 3G and 4G for equal data rates, the energy consumption increases with the amount of connections offloaded to WiFi, due to poor data rates obtained for WiFi in the streets. This suggests further deployment of WiFi access points or WiFi sharing incentives to increase data rates for WiFi and energy efficiency of mobile access.

Video streaming is the most popular application in today's mobile Internet and its growing demands and popularity put more and more load on cellular networks. In a recent trend to mitigate the cellular load, followed by many providers, users are offered to offload mobile connections to WiFi hotspots, which are predominately deployed in urban environments. In this work, we conduct a simulative performance evaluation of the impact of WiFi offloading on the Quality of Experience (QoE) of video streaming. The evaluation is based on connectivity measurements from a German city and uses a simple QoE model for estimating the perceived quality of video streaming. Our findings show that, despite its benefits for operators, offloading to WiFi has a negative impact on video streaming QoE for some users when 3G/4G coverage is available. Only in the case of 2G coverage, WiFi offloading can significantly improve the perceived quality for users.

Today’s Internet services are increasingly accessed from mobile devices, thus being responsible for growing load in mobile networks. At the same time, more and more WiFi routers are deployed such that a dense coverage of WiFi is available. Results from different related works suggest that there is a high potential of reducing load on the mobile networks by offloading data to WiFi networks, thereby improving mobile users’ quality of experience (QoE) with Internet services. Additionally, the storage of the router could be used for content caching and delivery close to the end user, which is more energy efficient compared to classical content servers, and saves costs for network operators by reducing traffic between autonomous systems. Going one step beyond, we foresee that merging these approaches and augmenting them with social information from online social networks (OSNs) will result both in even less costs for network operators and increased QoE of end users. Therefore, we propose home router sharing based on trust (HORST) - a socially-aware traffic management solution which targets three popular use cases: data offloading to WiFi, content caching/prefetching, and content delivery.

Application Aware Networking

Application-aware resource management is the approach to tailor access networks to have characteristics beneficial for the running applications and services. This is achieved through the monitoring and integration of key performance indicators from the application layer within the network resource management.

Using analytic and simulative approaches, we conduct analysis methods for network operators to quantify the performance gains of resource allocation algorithms that implement the application-aware concept.

In this report the main components of a testbed for evaluating the performance of resource management strategies has been validated. Application-aware resource management is a very important task since high quality applications like video streaming, social networking, and online gaming need a more intelligent bandwidth allocation policy than best effort to reach the highest possible Quality of Experience for all end users. Several tests have been performed to validate the determination of the web page size, Emma, Dory, and TraSh. It has been shown that the web page size has to be determined by measuring the used bandwidth at the router using a tool like ifstat since HTTP and TCP headers are not considered by tools within the browser like Firebug. Additionally, measurements provided that the latency caused by sending the resource allocation commands from Dory to TraSh is very small and should not affect the resource management results. Furthermore, the different random number generator distributions have been examined. It has been shown that Emma typically generates too many request arrivals with too low interarrival time so that requests are rejected. The validation of TraSh has shown that the time needed to realize the resource allocation adaption is pretty short and that there is no statistically significant difference between this delay for different capacities. On top of that, several problems of TraSh have been detected. Finally, the download times of web browsing requests have been measured and compared to simulation results. It has been shown that the download times in the measurement are much higher than the ones of the simulation if there is no initial delay implemented. By implementing an initial delay in the simulation it was shown that the download times converge. However, the initial delay needs to be further investigated since the elapsed time is not reliable known. On top of that, the problems of TraSh need more examination and resolving. Finally, the resource management strategies need to be implemented in Dory in order to measure their impact on download times for web browsing and buffering ratio for video streaming.